First, we read in the four tibbles from csv files.

library(tidyverse)
## ── Attaching packages ─────────────────────────────────────────── tidyverse 1.2.1 ──
## ✔ ggplot2 3.1.0     ✔ purrr   0.2.5
## ✔ tibble  1.4.2     ✔ dplyr   0.7.8
## ✔ tidyr   0.8.2     ✔ stringr 1.3.1
## ✔ readr   1.3.1     ✔ forcats 0.3.0
## ── Conflicts ────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
read_csv("2018-11-26_lrt-tibble.csv") -> r3104
## Parsed with column specification:
## cols(
##   gene1 = col_character(),
##   gene2 = col_character(),
##   lrt = col_double()
## )
read_csv("2018-12-03_lrt-tibble-run3105.csv") -> r3105
## Parsed with column specification:
## cols(
##   gene1 = col_character(),
##   gene2 = col_character(),
##   lrt = col_double()
## )
read_csv("2018-12-03_lrt-tibble-run3106.csv") -> r3106
## Parsed with column specification:
## cols(
##   gene1 = col_character(),
##   gene2 = col_character(),
##   lrt = col_double()
## )
read_csv("2018-12-03_lrt-tibble-run3107.csv") -> r3107
## Parsed with column specification:
## cols(
##   gene1 = col_character(),
##   gene2 = col_character(),
##   lrt = col_double()
## )
(bind_rows(r3104, r3105, r3106, r3107) -> rr)
## # A tibble: 316 x 3
##    gene1              gene2                lrt
##    <chr>              <chr>              <dbl>
##  1 ENSMUSG00000009378 ENSMUSG00000024887  17.6
##  2 ENSMUSG00000024766 ENSMUSG00000024887  38.4
##  3 ENSMUSG00000040565 ENSMUSG00000024887  37.3
##  4 ENSMUSG00000041731 ENSMUSG00000024887  44.0
##  5 ENSMUSG00000044026 ENSMUSG00000024887  58.1
##  6 ENSMUSG00000046138 ENSMUSG00000024887  13.3
##  7 ENSMUSG00000046324 ENSMUSG00000024887  26.9
##  8 ENSMUSG00000047298 ENSMUSG00000024887  29.6
##  9 ENSMUSG00000048120 ENSMUSG00000024887  44.9
## 10 ENSMUSG00000048612 ENSMUSG00000024887  64.3
## # ... with 306 more rows

Next, load the Attie data set from Data Dryad.

load("../data/Attie_DO378_eQTL_viewer_v1.Rdata")

We then work with the annotations object.

foo <- dataset.islet.rnaseq$annots %>%
  select(gene_id, symbol, start, end, middle) 
rr2 <- foo %>% 
  right_join(rr, by = c("gene_id" = "gene1")) %>%
  rename(gene1_symbol = symbol, gene1 = gene_id, gene1_start = start, gene1_end = end, gene1_middle = middle) %>%
  left_join(foo, by = c("gene2" = "gene_id")) %>%
  rename(gene2_symbol = symbol, gene2_start = start, gene2_end = end, gene2_middle = middle)

We need to add additional information from univariate QTL analyses for both gene1 and gene2.

bar <- dataset.islet.rnaseq$lod.peaks %>%
  filter(chrom == 19)
rr3 <- bar %>%
  right_join(rr2, by = c("annot.id" = "gene1")) %>%
  rename(gene1 = annot.id, gene1_marker.id = marker.id, gene1_pos = pos, gene1_lod = lod) %>%
  left_join(bar, by = c("gene2" = "annot.id")) %>%
  rename(gene2_marker.id = marker.id, gene2_pos = pos, gene2_lod = lod) %>%
  select(- chrom.x, - chrom.y)

LRT v position

library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
(pp <- rr3 %>%
  ggplot() + geom_point(aes(x = gene1_middle, y = lrt, colour = gene1_lod)) + geom_vline(aes(xintercept = gene2_middle)) + xlab("Chromosome 19 position") + ylab("Pleiotropy v separate QTL test statistic") + facet_grid(rows = gene2_symbol ~ .)
)

ggplotly(pp)

Arrange the panels by gene2 lod score

(nodup <- rr3 %>%
  filter(!duplicated(gene2)) %>%
  arrange(desc(gene2_lod)))
##                gene1 gene1_marker.id gene1_pos gene1_lod gene1_symbol
## 1 ENSMUSG00000009378     19_34707402   34.7074  25.54481     Slc16a12
## 2 ENSMUSG00000009378     19_34707402   34.7074  25.54481     Slc16a12
## 3 ENSMUSG00000009378     19_34707402   34.7074  25.54481     Slc16a12
## 4 ENSMUSG00000009378     19_34707402   34.7074  25.54481     Slc16a12
##   gene1_start gene1_end gene1_middle              gene2       lrt
## 1     34.6684  34.74729     34.70785 ENSMUSG00000024887 17.635215
## 2     34.6684  34.74729     34.70785 ENSMUSG00000024766  8.982999
## 3     34.6684  34.74729     34.70785 ENSMUSG00000087303  8.502847
## 4     34.6684  34.74729     34.70785 ENSMUSG00000089952 40.632300
##    gene2_symbol gene2_start gene2_end gene2_middle gene2_marker.id
## 1         Asah2    31.98465  32.06147     32.02306     19_32139966
## 2         Lipo1    33.51774  33.76195     33.63985     19_33671910
## 3         Lipo2    33.71967  33.76195     33.74081     19_33021521
## 4 4933413C19Rik    28.58053  28.58329     28.58191     19_28779826
##   gene2_pos gene2_lod
## 1  32.13997 101.19848
## 2  33.67191  85.46413
## 3  33.02152  77.20922
## 4  28.77983  60.41271
rr3 %>%
  mutate(gene2_symbol_factor = factor(gene2_symbol, levels = nodup$gene2_symbol, labels = paste0(nodup$gene2_symbol, " (", round(nodup$gene2_lod, 1), ")"))) %>%
  ggplot() + geom_point(aes(x = gene1_middle, y = lrt, colour = gene1_lod)) + geom_vline(aes(xintercept = gene2_middle)) + xlab("Chromosome 19 position") + ylab("Pleiotropy v separate QTL test statistic") + facet_grid(rows = gene2_symbol_factor ~ .)

ggsave(filename = "lrt-v-middle-of-gene.jpg", width = 7, height = 7, units = "in" )
ggsave(filename = "lrt-v-middle-of-gene.svg", width = 7, height = 7, units = "in" )
ggsave(filename = "lrt-v-middle-of-gene.eps", width = 7, height = 7, units = "in" )
ggsave(filename = "lrt-v-middle-of-gene.pdf", width = 7, height = 7, units = "in" )

LRT v univariate LOD by anchor gene

rr3 %>%
  mutate(gene2_symbol_factor = factor(gene2_symbol, levels = nodup$gene2_symbol, labels = paste0(nodup$gene2_symbol, " (", round(nodup$gene2_lod, 1), ")"))) %>%
  ggplot() + geom_point(aes(x = gene1_lod, y = lrt, colour = gene1_lod)) + xlab("Univariate LOD") + ylab("Pleiotropy v separate QTL test statistic") + facet_grid(rows = gene2_symbol_factor ~ .)

ggsave(filename = "lrt-v-univariate-lod.jpg", width = 7, height = 7, units = "in" )
ggsave(filename = "lrt-v-univariate-lod.svg", width = 7, height = 7, units = "in" )
ggsave(filename = "lrt-v-univariate-lod.eps", width = 7, height = 7, units = "in" )
ggsave(filename = "lrt-v-univariate-lod.pdf", width = 7, height = 7, units = "in" )

LRT v correlation (between fitted values)

We want to include our covariates in the fitted values calculations.

We’ll use the function qtl2::fit1.

library(qtl2)
## 
## Attaching package: 'qtl2'
## The following object is masked from 'package:readr':
## 
##     read_csv

rr3$gene1_marker.id tells us the name of the marker for gene1’s LOD peak. So, we’ll use it to get the matrix of allele dosages for input to fit1. Note that the third list element in the output of dimnames gives the marker_id values

dimnames(genoprobs$`19`)[[3]]
##    [1] "19_3000000"  "19_3024080"  "19_3048159"  "19_3072239" 
##    [5] "19_3096319"  "19_3120398"  "19_3455177"  "19_3471062" 
##    [9] "19_3486947"  "19_3647164"  "19_3651959"  "19_3656754" 
##   [13] "19_3985977"  "19_3987964"  "19_3989950"  "19_4267368" 
##   [17] "19_4271515"  "19_4275662"  "19_4611135"  "19_4614561" 
##   [21] "19_4617987"  "19_4621413"  "19_4624839"  "19_4628265" 
##   [25] "19_4978110"  "19_4985269"  "19_4988012"  "19_4990755" 
##   [29] "19_4993498"  "19_4996241"  "19_5291183"  "19_5294431" 
##   [33] "19_5297678"  "19_5380018"  "19_5398373"  "19_5416729" 
##   [37] "19_5487456"  "19_5501593"  "19_5515729"  "19_5860626" 
##   [41] "19_6205523"  "19_6365685"  "19_6383919"  "19_6392869" 
##   [45] "19_6396353"  "19_6399838"  "19_6403322"  "19_6405289" 
##   [49] "19_6405398"  "19_6405506"  "19_6407109"  "19_6411397" 
##   [53] "19_6415686"  "19_6539509"  "19_6540648"  "19_6541787" 
##   [57] "19_6542925"  "19_6544064"  "19_6545203"  "19_6697918" 
##   [61] "19_6740564"  "19_6783211"  "19_6957328"  "19_6971378" 
##   [65] "19_6985427"  "19_6999476"  "19_7049338"  "19_7142297" 
##   [69] "19_7234658"  "19_7249006"  "19_7264800"  "19_7281482" 
##   [73] "19_7563837"  "19_7569222"  "19_7572458"  "19_7575693" 
##   [77] "19_7578929"  "19_7582165"  "19_7585401"  "19_7588637" 
##   [81] "19_7591873"  "19_7595108"  "19_7598344"  "19_7601580" 
##   [85] "19_7605183"  "19_7704117"  "19_7705158"  "19_7706199" 
##   [89] "19_7896656"  "19_8202756"  "19_8508856"  "19_8618683" 
##   [93] "19_8660873"  "19_8696896"  "19_8732919"  "19_8768942" 
##   [97] "19_8794378"  "19_8798050"  "19_8801721"  "19_8805392" 
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##  [109] "19_8901909"  "19_8908134"  "19_8911650"  "19_8915165" 
##  [113] "19_8950378"  "19_8970029"  "19_8974262"  "19_8978494" 
##  [117] "19_9048898"  "19_9160399"  "19_9271900"  "19_9383401" 
##  [121] "19_9494902"  "19_9606403"  "19_9717904"  "19_9829405" 
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##  [577] "19_22894267" "19_22896884" "19_22899501" "19_22902118"
##  [581] "19_22904734" "19_22907351" "19_22909968" "19_22912585"
##  [585] "19_23342789" "19_23344991" "19_23347193" "19_23350338"
##  [589] "19_23359294" "19_23368249" "19_23376634" "19_23378430"
##  [593] "19_23380227" "19_23382024" "19_23383821" "19_23385618"
##  [597] "19_23868659" "19_23870233" "19_23871808" "19_23873382"
##  [601] "19_23874956" "19_23876530" "19_23917687" "19_23917752"
##  [605] "19_23917817" "19_23918175" "19_23925367" "19_23932559"
##  [609] "19_23988954" "19_23999542" "19_24010131" "19_24048529"
##  [613] "19_24058490" "19_24068452" "19_24078414" "19_24151901"
##  [617] "19_24152632" "19_24153364" "19_24154095" "19_24154826"
##  [621] "19_24155557" "19_24307454" "19_24332887" "19_24358320"
##  [625] "19_24382219" "19_24382803" "19_24383257" "19_24383620"
##  [629] "19_24383973" "19_24384325" "19_24384678" "19_24385081"
##  [633] "19_24386149" "19_24615398" "19_24623533" "19_24631669"
##  [637] "19_24646048" "19_24651380" "19_24655682" "19_24659983"
##  [641] "19_24661370" "19_24662705" "19_24664113" "19_24687303"
##  [645] "19_24729734" "19_24741223" "19_24745602" "19_24749981"
##  [649] "19_24754360" "19_24758506" "19_24762609" "19_24809264"
##  [653] "19_24874336" "19_24939407" "19_25004478" "19_25069550"
##  [657] "19_25094384" "19_25099509" "19_25100587" "19_25101665"
##  [661] "19_25102743" "19_25103820" "19_25104898" "19_25105976"
##  [665] "19_25108018" "19_25121124" "19_25124172" "19_25127220"
##  [669] "19_25131012" "19_25164884" "19_25174425" "19_25179040"
##  [673] "19_25181248" "19_25183456" "19_25185664" "19_25213450"
##  [677] "19_25225757" "19_25234484" "19_25240804" "19_25246239"
##  [681] "19_25251673" "19_25257107" "19_25262541" "19_25267975"
##  [685] "19_25275395" "19_25334339" "19_25375580" "19_25376735"
##  [689] "19_25377890" "19_25430429" "19_25445245" "19_25460061"
##  [693] "19_25474762" "19_25487123" "19_25500518" "19_25532899"
##  [697] "19_25565280" "19_25588832" "19_25599495" "19_25603706"
##  [701] "19_25607918" "19_25612129" "19_25616341" "19_25620552"
##  [705] "19_25624763" "19_25628975" "19_25633186" "19_25666923"
##  [709] "19_25705655" "19_25717202" "19_25724298" "19_25725807"
##  [713] "19_25727315" "19_25728824" "19_25730332" "19_25731841"
##  [717] "19_25733349" "19_25735898" "19_25748084" "19_25768410"
##  [721] "19_26254994" "19_26266960" "19_26278925" "19_26290890"
##  [725] "19_26302855" "19_26319456" "19_26336053" "19_26358240"
##  [729] "19_26382916" "19_26407592" "19_26432269" "19_26456945"
##  [733] "19_26480897" "19_26493222" "19_26505548" "19_26517873"
##  [737] "19_26527366" "19_26531695" "19_26535156" "19_26537007"
##  [741] "19_26538859" "19_26540710" "19_26542561" "19_26545521"
##  [745] "19_26587902" "19_26597204" "19_26606506" "19_26780867"
##  [749] "19_26890064" "19_26898865" "19_26899453" "19_26900040"
##  [753] "19_26900628" "19_26901215" "19_26901803" "19_26902390"
##  [757] "19_26905901" "19_26909897" "19_26986720" "19_26986998"
##  [761] "19_26987276" "19_26987554" "19_26988057" "19_26989339"
##  [765] "19_26991692" "19_27134620" "19_27135444" "19_27136269"
##  [769] "19_27381090" "19_27491492" "19_27491815" "19_27492138"
##  [773] "19_27536651" "19_27541278" "19_27545905" "19_27600054"
##  [777] "19_27604802" "19_27609550" "19_27662666" "19_27666470"
##  [781] "19_27670273" "19_27765374" "19_27811657" "19_27845848"
##  [785] "19_27880039" "19_27914230" "19_27948421" "19_27982612"
##  [789] "19_28008491" "19_28029718" "19_28037254" "19_28040225"
##  [793] "19_28043197" "19_28046169" "19_28049140" "19_28052112"
##  [797] "19_28055084" "19_28058055" "19_28061027" "19_28063998"
##  [801] "19_28066347" "19_28066583" "19_28066818" "19_28072860"
##  [805] "19_28081013" "19_28089166" "19_28102914" "19_28174689"
##  [809] "19_28190324" "19_28205958" "19_28276718" "19_28433340"
##  [813] "19_28433429" "19_28433519" "19_28433608" "19_28434587"
##  [817] "19_28435733" "19_28448079" "19_28502530" "19_28510569"
##  [821] "19_28518489" "19_28519386" "19_28519882" "19_28520378"
##  [825] "19_28520873" "19_28521369" "19_28521865" "19_28616997"
##  [829] "19_28711861" "19_28714631" "19_28717401" "19_28720171"
##  [833] "19_28730951" "19_28738709" "19_28746466" "19_28754224"
##  [837] "19_28759411" "19_28764166" "19_28768921" "19_28771409"
##  [841] "19_28773513" "19_28775617" "19_28777722" "19_28779826"
##  [845] "19_28781930" "19_28784034" "19_28786138" "19_28788242"
##  [849] "19_28790346" "19_28792691" "19_28939708" "19_28966461"
##  [853] "19_28979138" "19_28991815" "19_29004492" "19_29017169"
##  [857] "19_29029846" "19_29089975" "19_29244528" "19_29367672"
##  [861] "19_29395445" "19_29423219" "19_29450992" "19_29551490"
##  [865] "19_29699985" "19_29826905" "19_29868828" "19_29898206"
##  [869] "19_29927583" "19_29939649" "19_29945780" "19_29951911"
##  [873] "19_29958042" "19_30077227" "19_30166670" "19_30172106"
##  [877] "19_30177542" "19_30181155" "19_30181489" "19_30181824"
##  [881] "19_30198765" "19_30223345" "19_30235407" "19_30239974"
##  [885] "19_30244541" "19_30249108" "19_30253675" "19_30258951"
##  [889] "19_30270322" "19_30281692" "19_30322270" "19_30431504"
##  [893] "19_30438286" "19_30445067" "19_30451849" "19_30574351"
##  [897] "19_30671049" "19_30676947" "19_30682620" "19_30688292"
##  [901] "19_30695028" "19_30713997" "19_30732967" "19_30751936"
##  [905] "19_30770905" "19_30789875" "19_30822913" "19_30871283"
##  [909] "19_30875645" "19_30880007" "19_30948074" "19_31046561"
##  [913] "19_31048782" "19_31051002" "19_31053222" "19_31055443"
##  [917] "19_31057663" "19_31071475" "19_31171120" "19_31192742"
##  [921] "19_31202783" "19_31205947" "19_31209112" "19_31212276"
##  [925] "19_31215441" "19_31218605" "19_31221770" "19_31224934"
##  [929] "19_31228099" "19_31230108" "19_31231863" "19_31233618"
##  [933] "19_31235372" "19_31237127" "19_31238882" "19_31240713"
##  [937] "19_31243877" "19_31256110" "19_31277732" "19_31299355"
##  [941] "19_31320977" "19_31342599" "19_31364221" "19_31385844"
##  [945] "19_31438160" "19_31447073" "19_31449927" "19_31452781"
##  [949] "19_31455635" "19_31458735" "19_31466155" "19_31543307"
##  [953] "19_31558043" "19_31572779" "19_31587516" "19_31653333"
##  [957] "19_31694598" "19_31735863" "19_31777128" "19_31814623"
##  [961] "19_31816895" "19_31819167" "19_31821439" "19_31903650"
##  [965] "19_31927743" "19_31951836" "19_32054356" "19_32082893"
##  [969] "19_32111430" "19_32139966" "19_32168503" "19_32197040"
##  [973] "19_32252908" "19_32437021" "19_32455155" "19_32473289"
##  [977] "19_32491424" "19_32504071" "19_32509776" "19_32515498"
##  [981] "19_32521248" "19_32526997" "19_32532746" "19_32538495"
##  [985] "19_32553828" "19_32570619" "19_32587410" "19_32604201"
##  [989] "19_32636598" "19_32670407" "19_32704215" "19_32718469"
##  [993] "19_32723063" "19_32727657" "19_32732250" "19_32736844"
##  [997] "19_32748650" "19_32761174" "19_32773697" "19_32822102"
## [1001] "19_32864464" "19_32868705" "19_32872946" "19_32877188"
## [1005] "19_32881429" "19_32885671" "19_32917514" "19_32924036"
## [1009] "19_32930557" "19_32939488" "19_32948350" "19_32957213"
## [1013] "19_32963717" "19_32969874" "19_32976031" "19_32982188"
## [1017] "19_32988345" "19_32994548" "19_33003722" "19_33012897"
## [1021] "19_33021521" "19_33026219" "19_33031442" "19_33036664"
## [1025] "19_33043094" "19_33062165" "19_33070659" "19_33079153"
## [1029] "19_33180334" "19_33344193" "19_33508051" "19_33671910"
## [1033] "19_33835768" "19_33999626" "19_34034981" "19_34038599"
## [1037] "19_34042217" "19_34056872" "19_34062754" "19_34068636"
## [1041] "19_34096619" "19_34132400" "19_34168181" "19_34203962"
## [1045] "19_34282346" "19_34284190" "19_34286034" "19_34287879"
## [1049] "19_34289723" "19_34291568" "19_34317647" "19_34317951"
## [1053] "19_34318256" "19_34367155" "19_34427004" "19_34430634"
## [1057] "19_34434265" "19_34470255" "19_34528859" "19_34567314"
## [1061] "19_34605769" "19_34625797" "19_34628748" "19_34631700"
## [1065] "19_34643690" "19_34682145" "19_34701660" "19_34707402"
## [1069] "19_34713145" "19_34720970" "19_34786999" "19_34858054"
## [1073] "19_34880257" "19_34895441" "19_34910625" "19_34928124"
## [1077] "19_34999178" "19_35070233" "19_35121225" "19_35170959"
## [1081] "19_35220692" "19_35269815" "19_35286075" "19_35302336"
## [1085] "19_35318596" "19_35334856" "19_35351116" "19_35372084"
## [1089] "19_35423845" "19_35444219" "19_35456305" "19_35467489"
## [1093] "19_35478673" "19_35507967" "19_35546066" "19_35599959"
## [1097] "19_35678326" "19_35691162" "19_35703999" "19_35716835"
## [1101] "19_35792533" "19_35889492" "19_35986451" "19_36083410"
## [1105] "19_36131400" "19_36137515" "19_36137639" "19_36137762"
## [1109] "19_36137886" "19_36139028" "19_36140087" "19_36141145"
## [1113] "19_36142203" "19_36143262" "19_36144320" "19_36145379"
## [1117] "19_36146437" "19_36147495" "19_36148554" "19_36149612"
## [1121] "19_36150671" "19_36159084" "19_36172304" "19_36183233"
## [1125] "19_36188888" "19_36194542" "19_36236608" "19_36241204"
## [1129] "19_36245801" "19_36250397" "19_36260486" "19_36282620"
## [1133] "19_36303619" "19_36324618" "19_36345616" "19_36366615"
## [1137] "19_36387614" "19_36408613" "19_36465130" "19_36530966"
## [1141] "19_36596802" "19_36662639" "19_36728475" "19_36811333"
## [1145] "19_36824355" "19_36837376" "19_36892195" "19_36898397"
## [1149] "19_36899842" "19_36901287" "19_37069663" "19_37145334"
## [1153] "19_37221005" "19_37232749" "19_37239070" "19_37244725"
## [1157] "19_37250510" "19_37255312" "19_37257370" "19_37259427"
## [1161] "19_37261485" "19_37263542" "19_37265599" "19_37267657"
## [1165] "19_37270620" "19_37280817" "19_37299028" "19_37332743"
## [1169] "19_37343592" "19_37347317" "19_37351041" "19_37365990"
## [1173] "19_37399704" "19_37441724" "19_37464733" "19_37468012"
## [1177] "19_37471291" "19_37480300" "19_37481042" "19_37481785"
## [1181] "19_37482527" "19_37483269" "19_37484012" "19_37490782"
## [1185] "19_37514693" "19_37523302" "19_37530847" "19_37538393"
## [1189] "19_37545939" "19_37562181" "19_37587820" "19_37662709"
## [1193] "19_37712100" "19_37730810" "19_37734057" "19_37737304"
## [1197] "19_37760807" "19_37774974" "19_37784116" "19_37793257"
## [1201] "19_37809513" "19_37814843" "19_37819851" "19_37824858"
## [1205] "19_37853062" "19_37853668" "19_37854274" "19_37854880"
## [1209] "19_37855486" "19_37856092" "19_37856699" "19_37857305"
## [1213] "19_37857911" "19_37858517" "19_37859123" "19_37862448"
## [1217] "19_37867026" "19_37874206" "19_37882287" "19_37890369"
## [1221] "19_37904186" "19_37953577" "19_38002968" "19_38027116"
## [1225] "19_38030760" "19_38034404" "19_38051674" "19_38072015"
## [1229] "19_38072328" "19_38072640" "19_38115027" "19_38170198"
## [1233] "19_38173308" "19_38176417" "19_38231552" "19_38349714"
## [1237] "19_38413325" "19_38416110" "19_38418894" "19_38421678"
## [1241] "19_38424463" "19_38427247" "19_38430031" "19_38433852"
## [1245] "19_38438751" "19_38462963" "19_38581125" "19_38635154"
## [1249] "19_38642379" "19_38649604" "19_38678505" "19_38680948"
## [1253] "19_38683391" "19_38685833" "19_38700743" "19_38721543"
## [1257] "19_38742344" "19_38759034" "19_38759191" "19_38759348"
## [1261] "19_38774165" "19_38864633" "19_38955101" "19_39020200"
## [1265] "19_39084900" "19_39149600" "19_39214301" "19_39279001"
## [1269] "19_39343701" "19_39408401" "19_39473102" "19_39537802"
## [1273] "19_39602502" "19_39667203" "19_39731903" "19_39796603"
## [1277] "19_39861304" "19_39926004" "19_39995967" "19_40068263"
## [1281] "19_40140558" "19_40172080" "19_40181624" "19_40187003"
## [1285] "19_40191456" "19_40195909" "19_40200361" "19_40204814"
## [1289] "19_40209267" "19_40213719" "19_40218172" "19_40222624"
## [1293] "19_40226399" "19_40230005" "19_40233612" "19_40237218"
## [1297] "19_40240824" "19_40244431" "19_40248037" "19_40251643"
## [1301] "19_40290689" "19_40295305" "19_40295841" "19_40296378"
## [1305] "19_40310246" "19_40324645" "19_40339043" "19_40350097"
## [1309] "19_40353480" "19_40356863" "19_40360246" "19_40363629"
## [1313] "19_40367012" "19_40371460" "19_40380278" "19_40389096"
## [1317] "19_40395704" "19_40397237" "19_40398770" "19_40400303"
## [1321] "19_40405017" "19_40411406" "19_40417794" "19_40461519"
## [1325] "19_40478645" "19_40482029" "19_40485413" "19_40488797"
## [1329] "19_40492180" "19_40501522" "19_40586551" "19_40593927"
## [1333] "19_40601303" "19_40609798" "19_40613371" "19_40616944"
## [1337] "19_40671789" "19_40702444" "19_40733098" "19_40763753"
## [1341] "19_40891517" "19_40897216" "19_40902915" "19_40908613"
## [1345] "19_41300974" "19_41693336" "19_41697056" "19_41699543"
## [1349] "19_41702029" "19_41704516" "19_41707002" "19_41709489"
## [1353] "19_41712652" "19_41727128" "19_41787160" "19_41902281"
## [1357] "19_41921737" "19_41941194" "19_41960651" "19_41984459"
## [1361] "19_42051750" "19_42082710" "19_42090296" "19_42097882"
## [1365] "19_42104284" "19_42110548" "19_42116811" "19_42123075"
## [1369] "19_42127619" "19_42130861" "19_42134103" "19_42137345"
## [1373] "19_42140587" "19_42143829" "19_42148041" "19_42154304"
## [1377] "19_42160568" "19_42166831" "19_42173095" "19_42179385"
## [1381] "19_42185900" "19_42192414" "19_42198929" "19_42205444"
## [1385] "19_42242963" "19_42244896" "19_42246829" "19_42393401"
## [1389] "19_42413040" "19_42419737" "19_42420847" "19_42421957"
## [1393] "19_42429386" "19_42441259" "19_42447206" "19_42449000"
## [1397] "19_42450898" "19_42457626" "19_42482072" "19_42490357"
## [1401] "19_42498643" "19_42506929" "19_42515214" "19_42683343"
## [1405] "19_42702235" "19_42721127" "19_43007412" "19_43293697"
## [1409] "19_43447007" "19_43449943" "19_43452879" "19_43562978"
## [1413] "19_43564115" "19_43565252" "19_43566389" "19_43567647"
## [1417] "19_43569008" "19_43570370" "19_43571731" "19_43573093"
## [1421] "19_43574700" "19_43576854" "19_43579008" "19_43675948"
## [1425] "19_43682357" "19_43688765" "19_43877848" "19_43966844"
## [1429] "19_44055840" "19_44144836" "19_44148262" "19_44149285"
## [1433] "19_44150242" "19_44151198" "19_44152155" "19_44153112"
## [1437] "19_44154069" "19_44155025" "19_44155982" "19_44156939"
## [1441] "19_44157896" "19_44158853" "19_44159809" "19_44160766"
## [1445] "19_44161806" "19_44183541" "19_44231586" "19_44279632"
## [1449] "19_44300886" "19_44301828" "19_44302627" "19_44303425"
## [1453] "19_44304223" "19_44305021" "19_44305819" "19_44310169"
## [1457] "19_44317612" "19_44531915" "19_44565607" "19_44585982"
## [1461] "19_44587539" "19_44589095" "19_44590652" "19_44592656"
## [1465] "19_44598203" "19_44602247" "19_44604203" "19_44606158"
## [1469] "19_44608113" "19_44610068" "19_44612024" "19_44613979"
## [1473] "19_44615934" "19_44617890" "19_44619845" "19_44622567"
## [1477] "19_44627381" "19_44632503" "19_44638081" "19_44643658"
## [1481] "19_44648988" "19_44654049" "19_44659110" "19_44664171"
## [1485] "19_44669232" "19_44674293" "19_44679354" "19_44684415"
## [1489] "19_44689476" "19_44694536" "19_44699597" "19_44704658"
## [1493] "19_44714544" "19_44724814" "19_44732267" "19_44736252"
## [1497] "19_44740237" "19_44744222" "19_44748208" "19_44751173"
## [1501] "19_44753857" "19_44756541" "19_44759226" "19_44761910"
## [1505] "19_44764594" "19_44767279" "19_44775402" "19_44791894"
## [1509] "19_44809122" "19_44876370" "19_44907584" "19_44938797"
## [1513] "19_45140869" "19_45195450" "19_45223503" "19_45239557"
## [1517] "19_45250801" "19_45256200" "19_45261599" "19_45266998"
## [1521] "19_45272397" "19_45277796" "19_45289330" "19_45309785"
## [1525] "19_45330240" "19_45372147" "19_45477566" "19_45531844"
## [1529] "19_45565484" "19_45599407" "19_45633330" "19_45639911"
## [1533] "19_45644058" "19_45646586" "19_45649043" "19_45651499"
## [1537] "19_45653956" "19_45656412" "19_45658869" "19_45661326"
## [1541] "19_45666851" "19_45698652" "19_45701919" "19_45705185"
## [1545] "19_45708451" "19_45711717" "19_45714983" "19_45718250"
## [1549] "19_45721516" "19_45724782" "19_45736238" "19_45781269"
## [1553] "19_45786842" "19_45791893" "19_45796945" "19_45801996"
## [1557] "19_45807048" "19_45812099" "19_45817150" "19_45825610"
## [1561] "19_45834503" "19_46032167" "19_46036018" "19_46038163"
## [1565] "19_46040048" "19_46041933" "19_46043818" "19_46045704"
## [1569] "19_46047589" "19_46051370" "19_46055221" "19_46059073"
## [1573] "19_46065719" "19_46069280" "19_46072067" "19_46074854"
## [1577] "19_46077641" "19_46080428" "19_46083215" "19_46120578"
## [1581] "19_46493136" "19_46537847" "19_46543882" "19_46549918"
## [1585] "19_46564420" "19_46568980" "19_46572060" "19_46574158"
## [1589] "19_46576256" "19_46578354" "19_46580452" "19_46582597"
## [1593] "19_46597645" "19_46604111" "19_46605044" "19_46605978"
## [1597] "19_46691498" "19_46693309" "19_46695120" "19_46696931"
## [1601] "19_46698743" "19_46700554" "19_46847440" "19_46849425"
## [1605] "19_46851410" "19_46871050" "19_46935298" "19_46999546"
## [1609] "19_47151666" "19_47163007" "19_47174348" "19_47434097"
## [1613] "19_47434913" "19_47435729" "19_47504254" "19_47513922"
## [1617] "19_47523590" "19_47533257" "19_47574665" "19_47587472"
## [1621] "19_47600279" "19_47638134" "19_47645923" "19_47650780"
## [1625] "19_47655150" "19_47662797" "19_47672491" "19_47683605"
## [1629] "19_47691925" "19_47700246" "19_47762992" "19_47783936"
## [1633] "19_47804881" "19_47839103" "19_47842691" "19_47846278"
## [1637] "19_47849865" "19_47853452" "19_47857039" "19_47864387"
## [1641] "19_47872916" "19_47874119" "19_47875321" "19_47876524"
## [1645] "19_47877726" "19_47878929" "19_47891628" "19_47900207"
## [1649] "19_47908787" "19_47948122" "19_47956001" "19_47958630"
## [1653] "19_47961038" "19_47963445" "19_47965853" "19_47972651"
## [1657] "19_47982148" "19_47991645" "19_48105796" "19_48110549"
## [1661] "19_48115302" "19_48126955" "19_48128774" "19_48130593"
## [1665] "19_48179553" "19_48194106" "19_48206564" "19_48212807"
## [1669] "19_48214090" "19_48215373" "19_48216611" "19_48217814"
## [1673] "19_48219017" "19_48220220" "19_48222484" "19_48228013"
## [1677] "19_48235514" "19_48243016" "19_48250517" "19_48268078"
## [1681] "19_48281362" "19_48284536" "19_48286115" "19_48287155"
## [1685] "19_48288196" "19_48289236" "19_48290277" "19_48291317"
## [1689] "19_48293814" "19_48297988" "19_48302163" "19_48306337"
## [1693] "19_48310512" "19_48314687" "19_48318861" "19_48323036"
## [1697] "19_48327210" "19_48331497" "19_48454019" "19_48462226"
## [1701] "19_48466211" "19_48470196" "19_48474181" "19_48478166"
## [1705] "19_48483357" "19_48542194" "19_48549931" "19_48557668"
## [1709] "19_48565406" "19_48572851" "19_48577217" "19_48582842"
## [1713] "19_48588844" "19_48592506" "19_48596167" "19_48599828"
## [1717] "19_48603490" "19_48607151" "19_48610812" "19_48614473"
## [1721] "19_48618875" "19_48624220" "19_48629761" "19_48635301"
## [1725] "19_48640842" "19_48646383" "19_48651923" "19_48664414"
## [1729] "19_48692978" "19_48849777" "19_48869903" "19_48890029"
## [1733] "19_48974128" "19_49086420" "19_49198712" "19_49311004"
## [1737] "19_49423296" "19_49543545" "19_49544273" "19_49544964"
## [1741] "19_49545656" "19_49546348" "19_49547040" "19_49548017"
## [1745] "19_49549414" "19_49550811" "19_49564533" "19_49615476"
## [1749] "19_49670319" "19_49733274" "19_49817592" "19_49830532"
## [1753] "19_49843471" "19_50003626" "19_50069021" "19_50134416"
## [1757] "19_50155694" "19_50159866" "19_50164037" "19_50168209"
## [1761] "19_50172380" "19_50176552" "19_50180723" "19_50184894"
## [1765] "19_50189066" "19_50259713" "19_50264289" "19_50268866"
## [1769] "19_50378607" "19_50383204" "19_50387801" "19_50392398"
## [1773] "19_50396995" "19_50400487" "19_50403367" "19_50406247"
## [1777] "19_50409127" "19_50462996" "19_50465696" "19_50468395"
## [1781] "19_50471095" "19_50473794" "19_50476494" "19_50479193"
## [1785] "19_50481893" "19_50484592" "19_50487292" "19_50489991"
## [1789] "19_50492691" "19_50504850" "19_50516947" "19_50529045"
## [1793] "19_50592127" "19_50607638" "19_50623150" "19_50664308"
## [1797] "19_50699806" "19_50735304" "19_50993238" "19_51251171"
## [1801] "19_51333605" "19_51416039" "19_51566123" "19_51638133"
## [1805] "19_51646396" "19_51654660" "19_51726293" "19_51758700"
## [1809] "19_51770971" "19_51783243" "19_51800940" "19_51808616"
## [1813] "19_51816292" "19_51884210" "19_51943715" "19_51953158"
## [1817] "19_51962600" "19_52042841" "19_52054349" "19_52060049"
## [1821] "19_52065749" "19_52071449" "19_52077149" "19_52085288"
## [1825] "19_52202298" "19_52361165" "19_52365723" "19_52370281"
## [1829] "19_52374840" "19_52660201" "19_52760041" "19_52859881"
## [1833] "19_52959721" "19_53007398" "19_53013845" "19_53019394"
## [1837] "19_53024943" "19_53047399" "19_53071343" "19_53095287"
## [1841] "19_53139792" "19_53180685" "19_53207640" "19_53226982"
## [1845] "19_53246324" "19_53254662" "19_53258720" "19_53262779"
## [1849] "19_53266837" "19_53270896" "19_53274954" "19_53279013"
## [1853] "19_53283071" "19_53287130" "19_53291188" "19_53295247"
## [1857] "19_53299305" "19_53303364" "19_53307422" "19_53311481"
## [1861] "19_53315539" "19_53319598" "19_53330205" "19_53349823"
## [1865] "19_53369441" "19_53386125" "19_53397827" "19_53409528"
## [1869] "19_53421229" "19_53464752" "19_53472233" "19_53474731"
## [1873] "19_53477076" "19_53479421" "19_53481766" "19_53487091"
## [1877] "19_53491849" "19_53495697" "19_53498057" "19_53499729"
## [1881] "19_53500907" "19_53502086" "19_53503265" "19_53504443"
## [1885] "19_53505622" "19_53506801" "19_53507979" "19_53509158"
## [1889] "19_53510337" "19_53511515" "19_53512694" "19_53520308"
## [1893] "19_53539722" "19_53559136" "19_53578550" "19_53597964"
## [1897] "19_53617378" "19_53633487" "19_53648724" "19_53655762"
## [1901] "19_53662163" "19_53668564" "19_53671682" "19_53673353"
## [1905] "19_53675025" "19_53676696" "19_53678368" "19_53680039"
## [1909] "19_53681711" "19_53683435" "19_53693804" "19_53709041"
## [1913] "19_53724279" "19_53740183" "19_53756107" "19_53775541"
## [1917] "19_53780383" "19_53785226" "19_53792133" "19_53801863"
## [1921] "19_53809523" "19_53816993" "19_53824415" "19_53831836"
## [1925] "19_53839768" "19_53848059" "19_53890890" "19_53902282"
## [1929] "19_53913674" "19_53925066" "19_53957601" "19_53988636"
## [1933] "19_54019671" "19_54050705" "19_54089091" "19_54137951"
## [1937] "19_54144811" "19_54151671" "19_54170876" "19_54183928"
## [1941] "19_54191325" "19_54198721" "19_54205723" "19_54208854"
## [1945] "19_54211984" "19_54215114" "19_54218245" "19_54221375"
## [1949] "19_54230027" "19_54242543" "19_54255060" "19_54267576"
## [1953] "19_54280092" "19_54292608" "19_54297736" "19_54302582"
## [1957] "19_54307427" "19_54312273" "19_54329847" "19_54396168"
## [1961] "19_54406109" "19_54416050" "19_54420207" "19_54420714"
## [1965] "19_54421104" "19_54421459" "19_54421814" "19_54422168"
## [1969] "19_54422523" "19_54422877" "19_54423232" "19_54423587"
## [1973] "19_54423941" "19_54424296" "19_54424650" "19_54425005"
## [1977] "19_54425360" "19_54425714" "19_54426069" "19_54426423"
## [1981] "19_54426778" "19_54427133" "19_54427487" "19_54427842"
## [1985] "19_54486519" "19_54610038" "19_54635701" "19_54661364"
## [1989] "19_54687027" "19_54712690" "19_54738353" "19_54764016"
## [1993] "19_55009633" "19_55010574" "19_55011515" "19_55044992"
## [1997] "19_55065565" "19_55084592" "19_55087527" "19_55089658"
## [2001] "19_55091788" "19_55103354" "19_55122380" "19_55141406"
## [2005] "19_55160432" "19_55163566" "19_55166003" "19_55168441"
## [2009] "19_55173647" "19_55187817" "19_55205721" "19_55206871"
## [2013] "19_55208021" "19_55213552" "19_55238417" "19_55248022"
## [2017] "19_55257627" "19_55268046" "19_55279147" "19_55296054"
## [2021] "19_55319229" "19_55342403" "19_55365578" "19_55388753"
## [2025] "19_55414600" "19_55564020" "19_55567675" "19_55571330"
## [2029] "19_55574985" "19_55578640" "19_55585351" "19_55720021"
## [2033] "19_55729222" "19_55738423" "19_55747623" "19_55753994"
## [2037] "19_55757480" "19_55760047" "19_55762075" "19_55764102"
## [2041] "19_55766129" "19_55768156" "19_55771124" "19_55781832"
## [2045] "19_55782023" "19_55782215" "19_55782407" "19_55782598"
## [2049] "19_55782790" "19_55784886" "19_55789674" "19_55794462"
## [2053] "19_55812700" "19_55848648" "19_55849595" "19_55850543"
## [2057] "19_55851490" "19_55888925" "19_55927304" "19_55965683"
## [2061] "19_55991669" "19_55995481" "19_55999293" "19_56004133"
## [2065] "19_56234131" "19_56245314" "19_56250793" "19_56256273"
## [2069] "19_56261752" "19_56267232" "19_56366770" "19_56378959"
## [2073] "19_56391149" "19_56429625" "19_56710817" "19_56722849"
## [2077] "19_56726168" "19_56729488" "19_56732808" "19_56736128"
## [2081] "19_56745545" "19_56755526" "19_56766151" "19_56816730"
## [2085] "19_56829638" "19_56842547" "19_56864837" "19_56888216"
## [2089] "19_56898933" "19_56902978" "19_56907023" "19_56911069"
## [2093] "19_56915114" "19_56926456" "19_56939263" "19_56952069"
## [2097] "19_56964875" "19_56977681" "19_56990487" "19_57008168"
## [2101] "19_57011516" "19_57014864" "19_57018211" "19_57021559"
## [2105] "19_57024907" "19_57028509" "19_57048463" "19_57049216"
## [2109] "19_57049969" "19_57052118" "19_57054758" "19_57056858"
## [2113] "19_57058958" "19_57061058" "19_57063158" "19_57065257"
## [2117] "19_57067357" "19_57071891" "19_57078895" "19_57117439"
## [2121] "19_57158866" "19_57187139" "19_57196518" "19_57205897"
## [2125] "19_57214943" "19_57217891" "19_57221164" "19_57225060"
## [2129] "19_57228955" "19_57232851" "19_57236746" "19_57240642"
## [2133] "19_57281424" "19_57363728" "19_57390927" "19_57408498"
## [2137] "19_57426069" "19_57443640" "19_57461212" "19_57478783"
## [2141] "19_57496354" "19_57513925" "19_57531497" "19_57549068"
## [2145] "19_57566639" "19_57592966" "19_57594810" "19_57596653"
## [2149] "19_57598497" "19_57600340" "19_57602184" "19_57605427"
## [2153] "19_57618381" "19_57631335" "19_57644288" "19_57715557"
## [2157] "19_57726606" "19_57728728" "19_57730293" "19_57731858"
## [2161] "19_57733423" "19_57734988" "19_57736553" "19_57738118"
## [2165] "19_57739683" "19_57741248" "19_57742813" "19_57744378"
## [2169] "19_57745943" "19_57747734" "19_57749751" "19_57751708"
## [2173] "19_57753627" "19_57755546" "19_57761663" "19_57779680"
## [2177] "19_57797698" "19_57815715" "19_57833732" "19_57851750"
## [2181] "19_57869767" "19_57942573" "19_57979900" "19_57984355"
## [2185] "19_57988810" "19_57993265" "19_57997720" "19_58002175"
## [2189] "19_58006630" "19_58011085" "19_58015307" "19_58017329"
## [2193] "19_58019351" "19_58021374" "19_58023396" "19_58086340"
## [2197] "19_58108781" "19_58130223" "19_58144430" "19_58158637"
## [2201] "19_58180023" "19_58198778" "19_58204473" "19_58206378"
## [2205] "19_58207023" "19_58207667" "19_58208311" "19_58208955"
## [2209] "19_58209599" "19_58210244" "19_58210888" "19_58211532"
## [2213] "19_58212176" "19_58212820" "19_58213465" "19_58214109"
## [2217] "19_58215546" "19_58222556" "19_58225136" "19_58227715"
## [2221] "19_58230295" "19_58232875" "19_58235454" "19_58238034"
## [2225] "19_58240614" "19_58243193" "19_58247095" "19_58290496"
## [2229] "19_58301415" "19_58312333" "19_58333733" "19_58350836"
## [2233] "19_58351548" "19_58352260" "19_58352972" "19_58353685"
## [2237] "19_58354397" "19_58355109" "19_58355821" "19_58356534"
## [2241] "19_58357246" "19_58357958" "19_58358670" "19_58359383"
## [2245] "19_58360095" "19_58360807" "19_58361519" "19_58362232"
## [2249] "19_58362944" "19_58363656" "19_58364368" "19_58365081"
## [2253] "19_58365793" "19_58367179" "19_58368753" "19_58370327"
## [2257] "19_58371901" "19_58373475" "19_58380166" "19_58404015"
## [2261] "19_58414558" "19_58425101" "19_58435644" "19_58446187"
## [2265] "19_58456730" "19_58463035" "19_58463677" "19_58464319"
## [2269] "19_58464962" "19_58465604" "19_58466246" "19_58466889"
## [2273] "19_58467531" "19_58468173" "19_58468816" "19_58469458"
## [2277] "19_58470100" "19_58470742" "19_58475704" "19_58493750"
## [2281] "19_58523319" "19_58540234" "19_58554038" "19_58566202"
## [2285] "19_58568109" "19_58569557" "19_58571006" "19_58572454"
## [2289] "19_58573902" "19_58575351" "19_58576799" "19_58578247"
## [2293] "19_58579696" "19_58581144" "19_58584194" "19_58587673"
## [2297] "19_58591152" "19_58594631" "19_58598109" "19_58601588"
## [2301] "19_58605067" "19_58608546" "19_58612025" "19_58615504"
## [2305] "19_58618983" "19_58622461" "19_58625940" "19_58629419"
## [2309] "19_58632898" "19_58636377" "19_58639856" "19_58643335"
## [2313] "19_58646813" "19_58650377" "19_58653952" "19_58657527"
## [2317] "19_58661103" "19_58664678" "19_58668253" "19_58671828"
## [2321] "19_58675404" "19_58678979" "19_58682554" "19_58686130"
## [2325] "19_58692517" "19_58698962" "19_58705407" "19_58711852"
## [2329] "19_58718297" "19_58722782" "19_58727204" "19_58731625"
## [2333] "19_58738847" "19_58746340" "19_58753833" "19_58910655"
## [2337] "19_58919224" "19_58925050" "19_58930876" "19_58965288"
## [2341] "19_59002723" "19_59018856" "19_59034988" "19_59051121"
## [2345] "19_59067254" "19_59087966" "19_59110726" "19_59133487"
## [2349] "19_59140972" "19_59143751" "19_59146530" "19_59149310"
## [2353] "19_59152089" "19_59154868" "19_59157647" "19_59160427"
## [2357] "19_59163206" "19_59165985" "19_59168765" "19_59171544"
## [2361] "19_59174323" "19_59177745" "19_59200506" "19_59226685"
## [2365] "19_59235822" "19_59244959" "19_59352728" "19_59363450"
## [2369] "19_59366261" "19_59369073" "19_59371884" "19_59374696"
## [2373] "19_59453759" "19_59458391" "19_59461869" "19_59465346"
## [2377] "19_59468823" "19_59474449" "19_59628882" "19_59638759"
## [2381] "19_59648637" "19_59901959" "19_59902196" "19_59902433"
## [2385] "19_60029239" "19_60050528" "19_60056498" "19_60058841"
## [2389] "19_60061183" "19_60063526" "19_60065868" "19_60068211"
## [2393] "19_60070504" "19_60071463" "19_60072422" "19_60073381"
## [2397] "19_60099941" "19_60128690" "19_60137675" "19_60142397"
## [2401] "19_60147118" "19_60157040" "19_60267433" "19_60308575"
## [2405] "19_60349717" "19_60365500" "19_60370464" "19_60375427"
## [2409] "19_60379939" "19_60382982" "19_60384178" "19_60385374"
## [2413] "19_60386571" "19_60387767" "19_60388963" "19_60391609"
## [2417] "19_60399219" "19_60406829" "19_60429719" "19_60447052"
## [2421] "19_60459406" "19_60471761" "19_60485339" "19_60745900"
## [2425] "19_61006460" "19_61009453" "19_61012445" "19_61272832"
## [2429] "19_61296911" "19_61320991" "19_61345071" "19_61369150"
## [2433] "19_61393230" "19_61417310"

Let’s read in the expr data for the 80 traits of interest.

readRDS("../data/chr19_expr_10mb.rds") -> expr
colnames(expr)
##  [1] "ENSMUSG00000009378" "ENSMUSG00000012443" "ENSMUSG00000013663"
##  [4] "ENSMUSG00000016495" "ENSMUSG00000024766" "ENSMUSG00000024771"
##  [7] "ENSMUSG00000024774" "ENSMUSG00000024776" "ENSMUSG00000024778"
## [10] "ENSMUSG00000024780" "ENSMUSG00000024781" "ENSMUSG00000024782"
## [13] "ENSMUSG00000024805" "ENSMUSG00000024817" "ENSMUSG00000024827"
## [16] "ENSMUSG00000024863" "ENSMUSG00000024867" "ENSMUSG00000024878"
## [19] "ENSMUSG00000024887" "ENSMUSG00000024921" "ENSMUSG00000024935"
## [22] "ENSMUSG00000024987" "ENSMUSG00000024992" "ENSMUSG00000024998"
## [25] "ENSMUSG00000024999" "ENSMUSG00000025006" "ENSMUSG00000025008"
## [28] "ENSMUSG00000025010" "ENSMUSG00000025020" "ENSMUSG00000025159"
## [31] "ENSMUSG00000025171" "ENSMUSG00000025176" "ENSMUSG00000033207"
## [34] "ENSMUSG00000033610" "ENSMUSG00000033863" "ENSMUSG00000034321"
## [37] "ENSMUSG00000035049" "ENSMUSG00000038658" "ENSMUSG00000040105"
## [40] "ENSMUSG00000040451" "ENSMUSG00000040565" "ENSMUSG00000041731"
## [43] "ENSMUSG00000044026" "ENSMUSG00000046138" "ENSMUSG00000046324"
## [46] "ENSMUSG00000047298" "ENSMUSG00000048120" "ENSMUSG00000048612"
## [49] "ENSMUSG00000049670" "ENSMUSG00000050370" "ENSMUSG00000050957"
## [52] "ENSMUSG00000052085" "ENSMUSG00000052595" "ENSMUSG00000054178"
## [55] "ENSMUSG00000054237" "ENSMUSG00000055044" "ENSMUSG00000056078"
## [58] "ENSMUSG00000059363" "ENSMUSG00000061132" "ENSMUSG00000064202"
## [61] "ENSMUSG00000067242" "ENSMUSG00000067279" "ENSMUSG00000071573"
## [64] "ENSMUSG00000074896" "ENSMUSG00000074922" "ENSMUSG00000079339"
## [67] "ENSMUSG00000079344" "ENSMUSG00000085003" "ENSMUSG00000086812"
## [70] "ENSMUSG00000087112" "ENSMUSG00000087303" "ENSMUSG00000087579"
## [73] "ENSMUSG00000089952" "ENSMUSG00000090369" "ENSMUSG00000090673"
## [76] "ENSMUSG00000094707" "ENSMUSG00000097558" "ENSMUSG00000097787"
## [79] "ENSMUSG00000097792" "ENSMUSG00000097930"
ann80 <- rr3 %>%
  select(gene1, gene1_marker.id, gene1_symbol) %>%
  filter(!duplicated(gene1_symbol))
fit1_out <- list()
for (i in 1:80){
  pr <- genoprobs$`19`[ , , which(dimnames(genoprobs$`19`)[[3]] == ann80$gene1_marker.id[i])]
  fit1_out[[i]] <- fit1(genoprobs = pr, 
       pheno = expr[, which(colnames(expr) == ann80$gene1[i]), drop = FALSE], 
       kinship = K$`19`, 
       addcovar = dataset.islet.rnaseq$covar, 
       reml = TRUE
       )
}
sapply(FUN = function(x)x$fitted, X = fit1_out) -> fits
colnames(fits)
## NULL
colnames(fits) <- ann80$gene1
rownames(fits)
##   [1] "DO021" "DO022" "DO023" "DO024" "DO025" "DO026" "DO027" "DO030"
##   [9] "DO031" "DO032" "DO033" "DO034" "DO035" "DO036" "DO037" "DO038"
##  [17] "DO039" "DO040" "DO041" "DO042" "DO044" "DO046" "DO047" "DO048"
##  [25] "DO049" "DO050" "DO051" "DO052" "DO053" "DO054" "DO055" "DO056"
##  [33] "DO057" "DO058" "DO059" "DO060" "DO061" "DO062" "DO063" "DO064"
##  [41] "DO065" "DO066" "DO067" "DO068" "DO069" "DO070" "DO071" "DO072"
##  [49] "DO073" "DO075" "DO076" "DO078" "DO079" "DO080" "DO081" "DO082"
##  [57] "DO083" "DO084" "DO085" "DO086" "DO087" "DO088" "DO089" "DO090"
##  [65] "DO091" "DO092" "DO094" "DO095" "DO096" "DO097" "DO098" "DO099"
##  [73] "DO100" "DO101" "DO102" "DO103" "DO104" "DO105" "DO106" "DO107"
##  [81] "DO108" "DO109" "DO111" "DO112" "DO113" "DO114" "DO115" "DO116"
##  [89] "DO118" "DO119" "DO120" "DO121" "DO122" "DO123" "DO124" "DO125"
##  [97] "DO126" "DO128" "DO129" "DO130" "DO131" "DO132" "DO133" "DO137"
## [105] "DO138" "DO140" "DO141" "DO142" "DO143" "DO144" "DO145" "DO146"
## [113] "DO147" "DO148" "DO149" "DO150" "DO151" "DO152" "DO153" "DO154"
## [121] "DO155" "DO156" "DO157" "DO158" "DO159" "DO160" "DO161" "DO162"
## [129] "DO163" "DO164" "DO165" "DO166" "DO167" "DO168" "DO169" "DO170"
## [137] "DO171" "DO172" "DO173" "DO174" "DO175" "DO176" "DO177" "DO178"
## [145] "DO179" "DO180" "DO181" "DO182" "DO183" "DO184" "DO185" "DO186"
## [153] "DO187" "DO188" "DO189" "DO190" "DO191" "DO192" "DO193" "DO194"
## [161] "DO195" "DO196" "DO197" "DO199" "DO200" "DO201" "DO202" "DO203"
## [169] "DO204" "DO205" "DO206" "DO207" "DO208" "DO209" "DO210" "DO211"
## [177] "DO212" "DO213" "DO214" "DO215" "DO216" "DO217" "DO218" "DO219"
## [185] "DO220" "DO221" "DO222" "DO223" "DO224" "DO225" "DO226" "DO227"
## [193] "DO228" "DO229" "DO230" "DO231" "DO232" "DO233" "DO234" "DO235"
## [201] "DO236" "DO237" "DO238" "DO239" "DO240" "DO241" "DO242" "DO243"
## [209] "DO244" "DO245" "DO246" "DO247" "DO249" "DO250" "DO251" "DO252"
## [217] "DO253" "DO254" "DO255" "DO256" "DO257" "DO258" "DO259" "DO260"
## [225] "DO261" "DO262" "DO263" "DO264" "DO265" "DO266" "DO267" "DO268"
## [233] "DO269" "DO270" "DO271" "DO272" "DO273" "DO274" "DO275" "DO276"
## [241] "DO277" "DO278" "DO279" "DO280" "DO281" "DO282" "DO283" "DO284"
## [249] "DO285" "DO286" "DO287" "DO288" "DO289" "DO290" "DO291" "DO292"
## [257] "DO293" "DO294" "DO295" "DO296" "DO297" "DO298" "DO299" "DO300"
## [265] "DO301" "DO302" "DO303" "DO304" "DO305" "DO306" "DO307" "DO308"
## [273] "DO309" "DO310" "DO311" "DO312" "DO313" "DO314" "DO315" "DO316"
## [281] "DO317" "DO318" "DO319" "DO320" "DO321" "DO322" "DO323" "DO324"
## [289] "DO325" "DO326" "DO327" "DO328" "DO329" "DO330" "DO331" "DO332"
## [297] "DO333" "DO335" "DO336" "DO337" "DO338" "DO339" "DO340" "DO342"
## [305] "DO343" "DO344" "DO345" "DO346" "DO347" "DO348" "DO349" "DO350"
## [313] "DO352" "DO353" "DO354" "DO355" "DO356" "DO357" "DO358" "DO359"
## [321] "DO360" "DO361" "DO362" "DO363" "DO364" "DO365" "DO366" "DO367"
## [329] "DO368" "DO369" "DO370" "DO371" "DO373" "DO374" "DO375" "DO376"
## [337] "DO377" "DO378" "DO379" "DO380" "DO381" "DO382" "DO383" "DO384"
## [345] "DO385" "DO386" "DO387" "DO388" "DO389" "DO390" "DO391" "DO392"
## [353] "DO393" "DO394" "DO395" "DO396" "DO397" "DO398" "DO399" "DO400"
## [361] "DO401" "DO402" "DO403" "DO404" "DO405" "DO406" "DO407" "DO408"
## [369] "DO409" "DO410" "DO411" "DO412" "DO413" "DO414" "DO415" "DO416"
## [377] "DO417" "DO420"
cor(fits) -> fitcor

Create and populate the fit1_corr with correlations

rr3$fit1_corr <- NA
for (i in 1:nrow(rr3)){
  rr3$fit1_corr[i] <- fitcor[which(rownames(fitcor) == rr3$gene1[i]), which(colnames(fitcor) == rr3$gene2[i])]
}
rr3 %>%
  mutate(gene2_symbol_factor = factor(gene2_symbol, levels = nodup$gene2_symbol, labels = paste0(nodup$gene2_symbol, " (", round(nodup$gene2_lod, 1), ")"))) %>%
  ggplot() + geom_point(aes(x = fit1_corr, y = lrt, colour = gene1_lod)) + xlab("Fitted values correlation") + ylab("Pleiotropy v separate QTL test statistic") + facet_grid(rows = gene2_symbol_factor ~ .)

ggsave(filename = "lrt-v-corr.jpg", width = 7, height = 7, units = "in" )
ggsave(filename = "lrt-v-corr.svg", width = 7, height = 7, units = "in" )
ggsave(filename = "lrt-v-corr.eps", width = 7, height = 7, units = "in" )
ggsave(filename = "lrt-v-corr.pdf", width = 7, height = 7, units = "in" )

Allele effects plots

We now create founder allele effects plots for the four anchor genes: Asah2, Lipo1, Lipo2, and the fourth gene.

We first need the Ensembl ids for the four genes.

unique(rr2$gene2) -> id4
unique(rr2$gene2_symbol) -> id4_symbols
expr4 <- expr[ , colnames(expr) %in% id4]
out4 <- list()
for (i in 1:4){
  out4[[i]] <- scan1coef(genoprobs = list(genoprobs$`19`), 
            pheno = expr4[ , i, drop = FALSE], 
            kinship = K$`19`, 
            addcovar = dataset.islet.rnaseq$covar, 
            reml = TRUE)
}
for (i in 1:4){
  if (i == 1){
    leg <- "bottomright"
  } else {
    leg <- NULL
  }
  uni_peak_pos <- nodup %>%
    filter(gene2_symbol == id4_symbols[i]) %>%
    select(gene2_pos) %>%
    unlist()
  epsfn <- paste0("allele_effects_", id4_symbols[i], ".eps")
  pdffn <- paste0("allele_effects_", id4_symbols[i], ".pdf")
  setEPS()
  postscript(epsfn, 
             width = 6, 
             height = 4
             )
  plot_coefCC(out4[[i]], 
              map = map[19], 
              legend = leg, 
              ylim = c(- 2.5, 2.5), 
              legend_ncol = 4
              )
  text(x = 50, 
       y = 2, 
       labels = id4_symbols[i]
       )
  #abline(v = uni_peak_pos)
  dev.off()
  pdf(pdffn, 
      width = 6, 
      height = 4
      )
  cex_all <- 0.75
  par(cex = cex_all)
  plot_coefCC(out4[[i]], 
              map = map[19], 
              legend = leg, 
              ylim = c(- 2.5, 3), 
              legend_ncol = 4, 
              )
  text(x = 50, 
       y = 2, 
       labels = id4_symbols[i],
       cex = 1.5 / cex_all
       )
  #abline(v = uni_peak_pos)
  dev.off()
}

Make tables for inclusion in .tex file

library(xtable)

We make a xtable for the

nodup %>%
  select(gene2_symbol, gene2_start, gene2_end, gene2_pos, gene2_lod) %>%
  rename(symbol = gene2_symbol, start = gene2_start, end = gene2_end, peak_position = gene2_pos, lod = gene2_lod) %>% 
  xtable() %>%
  print.xtable(include.rownames = FALSE)
% latex table generated in R 3.5.2 by xtable 1.8-3 package % Thu Jan 3 16:44:56 2019

Make the table for the 76 non-anchor genes.

rr3 %>%
  select(gene1_symbol, gene1_start, gene1_end, gene1_pos, gene1_lod) %>%
  rename(symbol = gene1_symbol, start = gene1_start, end = gene1_end, peak_position = gene1_pos, lod = gene1_lod) %>%
  filter(!duplicated(symbol)) %>%
  filter(!(symbol %in% c("Asah2", "Lipo1", "Lipo2", "4933413C19Rik"))) %>%
  arrange(desc(lod))%>% 
  xtable() %>%
  print.xtable(include.rownames = FALSE, size = "\\tiny")
## % latex table generated in R 3.5.2 by xtable 1.8-3 package
## % Thu Jan  3 16:44:56 2019
## \begin{table}[ht]
## \centering
## \begingroup\tiny
## \begin{tabular}{lrrrr}
##   \hline
## symbol & start & end & peak\_position & lod \\ 
##   \hline
## C030046E11Rik & 29.52 & 29.61 & 29.55 & 95.58 \\ 
##   Tctn3 & 40.60 & 40.61 & 40.59 & 90.00 \\ 
##   Gm7237 & 33.41 & 33.42 & 33.67 & 74.61 \\ 
##   Lipo4 & 33.50 & 33.52 & 34.00 & 68.23 \\ 
##   Dock8 & 25.00 & 25.20 & 25.07 & 63.17 \\ 
##   Sorbs1 & 40.30 & 40.40 & 40.48 & 61.89 \\ 
##   Lipm & 34.10 & 34.12 & 34.06 & 58.43 \\ 
##   Blnk & 40.93 & 40.99 & 40.76 & 57.16 \\ 
##   A830019P07Rik & 35.84 & 35.92 & 35.60 & 55.54 \\ 
##   Uhrf2 & 30.03 & 30.09 & 29.96 & 54.40 \\ 
##   Mbl2 & 30.23 & 30.24 & 30.18 & 52.81 \\ 
##   Myof & 37.90 & 38.04 & 38.05 & 48.46 \\ 
##   Gm27042 & 40.59 & 40.59 & 40.61 & 44.27 \\ 
##   Btaf1 & 36.93 & 37.01 & 36.90 & 41.25 \\ 
##   Hoga1 & 42.05 & 42.07 & 42.09 & 41.23 \\ 
##   Ppp1r3c & 36.73 & 36.74 & 36.53 & 40.69 \\ 
##   Pcgf5 & 36.38 & 36.46 & 36.24 & 40.06 \\ 
##   Slc35g1 & 38.40 & 38.41 & 38.35 & 38.11 \\ 
##   Pten & 32.76 & 32.83 & 32.77 & 37.95 \\ 
##   Gldc & 30.10 & 30.18 & 30.17 & 36.26 \\ 
##   Lgi1 & 38.26 & 38.31 & 38.17 & 34.91 \\ 
##   C330002G04Rik & 23.04 & 23.08 & 23.34 & 34.84 \\ 
##   Ppapdc2 & 28.96 & 28.97 & 29.09 & 34.71 \\ 
##   Gm8978 & 33.61 & 33.63 & 33.03 & 34.59 \\ 
##   Mms19 & 41.94 & 41.98 & 41.98 & 32.03 \\ 
##   Ankrd22 & 34.12 & 34.17 & 34.04 & 31.83 \\ 
##   Cdc37l1 & 28.99 & 29.02 & 29.03 & 31.14 \\ 
##   Sgms1 & 32.12 & 32.39 & 32.11 & 30.10 \\ 
##   Entpd1 & 40.61 & 40.74 & 40.50 & 29.73 \\ 
##   Cbwd1 & 24.92 & 24.96 & 24.73 & 29.65 \\ 
##   Gm14446 & 34.59 & 34.60 & 34.28 & 27.65 \\ 
##   Ermp1 & 29.61 & 29.65 & 29.70 & 26.57 \\ 
##   Gm9938 & 23.72 & 23.73 & 23.87 & 26.46 \\ 
##   Insl6 & 29.32 & 29.33 & 29.37 & 26.23 \\ 
##   Slc16a12 & 34.67 & 34.75 & 34.71 & 25.54 \\ 
##   Pgm5 & 24.68 & 24.86 & 25.00 & 24.30 \\ 
##   Morn4 & 42.07 & 42.09 & 41.79 & 23.86 \\ 
##   Exosc1 & 41.92 & 41.93 & 42.10 & 23.28 \\ 
##   Smarca2 & 26.61 & 26.78 & 26.59 & 23.25 \\ 
##   4930418C01Rik & 24.42 & 24.43 & 23.92 & 23.10 \\ 
##   2700046G09Rik & 32.39 & 32.39 & 32.25 & 23.02 \\ 
##   Kcnv2 & 27.32 & 27.34 & 27.14 & 22.88 \\ 
##   1500017E21Rik & 36.61 & 36.71 & 37.07 & 22.78 \\ 
##   Fra10ac1 & 38.19 & 38.22 & 38.35 & 22.48 \\ 
##   Rnls & 33.14 & 33.39 & 34.17 & 21.94 \\ 
##   Noc3l & 38.79 & 38.82 & 40.20 & 21.67 \\ 
##   Pip5k1b & 24.29 & 24.56 & 24.15 & 21.62 \\ 
##   Plgrkt & 29.35 & 29.37 & 29.37 & 20.65 \\ 
##   Ifit3 & 34.58 & 34.59 & 34.28 & 20.45 \\ 
##   Fas & 34.29 & 34.33 & 34.20 & 19.65 \\ 
##   Slit1 & 41.60 & 41.74 & 41.70 & 18.95 \\ 
##   Rrp12 & 41.86 & 41.90 & 41.71 & 18.09 \\ 
##   Ak3 & 29.02 & 29.05 & 29.55 & 16.90 \\ 
##   A1cf & 31.87 & 31.95 & 32.11 & 15.56 \\ 
##   4430402I18Rik & 28.90 & 28.97 & 29.37 & 15.43 \\ 
##   Pdlim1 & 40.22 & 40.27 & 40.25 & 15.25 \\ 
##   Gm26902 & 34.47 & 34.48 & 36.15 & 14.26 \\ 
##   Plce1 & 38.48 & 38.79 & 38.42 & 14.26 \\ 
##   Slc1a1 & 28.84 & 28.91 & 28.97 & 14.18 \\ 
##   Fam122a & 24.48 & 24.48 & 24.08 & 14.07 \\ 
##   Lipa & 34.49 & 34.53 & 34.29 & 14.06 \\ 
##   Mamdc2 & 23.30 & 23.45 & 23.35 & 13.12 \\ 
##   Kif11 & 37.38 & 37.42 & 37.33 & 12.93 \\ 
##   4933411K16Rik & 42.05 & 42.05 & 42.08 & 12.92 \\ 
##   Ccnj & 40.83 & 40.85 & 40.59 & 12.19 \\ 
##   Gm340 & 41.58 & 41.59 & 41.30 & 12.17 \\ 
##   Fxn & 24.26 & 24.28 & 24.31 & 12.07 \\ 
##   Stambpl1 & 34.19 & 34.24 & 34.28 & 11.62 \\ 
##   Pde6c & 38.13 & 38.18 & 38.07 & 11.54 \\ 
##   Cyp26a1 & 37.70 & 37.70 & 37.48 & 11.35 \\ 
##   Ch25h & 34.47 & 34.48 & 32.50 & 10.74 \\ 
##   Pank1 & 34.81 & 34.88 & 35.55 & 10.61 \\ 
##   9930021J03Rik & 29.71 & 29.81 & 28.71 & 10.32 \\ 
##   Klf9 & 23.14 & 23.17 & 23.34 & 10.26 \\ 
##   Ubtd1 & 41.98 & 42.03 & 41.71 & 10.25 \\ 
##   Lipk & 34.01 & 34.05 & 34.29 & 10.23 \\ 
##    \hline
## \end{tabular}
## \endgroup
## \end{table}